Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Real-time systems perform analytics to correlate and predict event streams. Machine learning or classification methods are often applied to real-time data analytics. Often, this introduces problems if the underlying data distribution is likely to change over time. For example, companies collect an increasing amount of data (e.g., sales figures, customer data, etc.) to find patterns in customer behavior and to predict future sales, and this data generally changes over time.
As customer behavior tends to change over time, the prediction model should adapt accordingly. Adaptive data analytics systems often utilize batch processing systems. In batch analysis it is fairly easy to divide data into discrete time periods and perform classifier rediscovery or comparisons that are not in real-time. Typically, however, real-time streams are effectively infinite in length and continuous, and therefore it is difficult to adopt streaming adaptive solutions at scale.